Gait synthesis for a biped robot climbing sloping surfaces using neural networks. I. Static learning

A neural network mechanism is proposed to modify the rhythmic motion (gait) of a two-legged robot when walking on sloping surfaces using a sensory input. The robot starts walking on a terrain with no previous knowledge, but accumulates walking experience during walking, thus constantly improving its walking gait. The proposed network consists of 20 reciprocally inhibited and excited neurons. An unsupervised learning rule was implemented using reinforcement signals. Two learning algorithms are introduced. The primary concern in the first algorithm was the speed of gait modification, whereas the second algorithm provided a solution with minimum energy consumption. A static learning approach where learning takes place only at prespecified moments is proposed